Development of Clinical-Grade Antibodies against Tumor-Specific Mutations to Target Neuroblastoma

Stephanie C. Pero, Abhinav B. Nagulapally, Linda Mei, Fan Zhang, Giselle S. Sholler, David N. Krag, Girja S. Shukla

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Objective. Tumor heterogeneity is a fundamental problem in treating cancer with monotargeting therapy, including chemical, antibody, and T cell therapies. Our goal is to target multiple mutated peptides found in a patient’s cancer to increase antibody therapy effectiveness. Methods. Tumor samples were derived from patients with neuroblastoma. Whole-exome sequencing was performed of tumor and normal cells. Mutated proteins with missense mutations were selected from the patient tumor. These mutated proteins were further selected for the presence of missense mutations in the outer cell surface. Peptides representing a mutated section of the proteins were used for vaccinating rabbits and generating anti-peptide antibodies. The binding of individual polyclonal antibodies (pAbs) and the mixtures of pAbs were determined against the patient’s tumor as cultured neuroblastoma cells and in a murine xenograft model. Antibodies were prepared according to FDA requirements of a phase I clinical protocol. Results. All of the generated rabbit pAbs bound with high affinity to the corresponding peptide used for vaccination. The pAbs also bound to low passage neuroblastoma cells. Mixed as cocktails, the pAbs had substantially increased binding to cells and bound well to the xenograft tissue. No binding was observed to the panel of normal human tissues. Preparation of pAbs by an academic lab to clinical-grade was approved by FDA for phase I clinical trial. Conclusion. We describe a new strategy to make customized antibodies for individual cancer patients and present the data required to meet FDA specifications to begin a phase I clinical trial.

Original languageEnglish (US)
Pages (from-to)349-358
Number of pages10
JournalAnnals of clinical and laboratory science
Volume52
Issue number3
StatePublished - May 2022

All Science Journal Classification (ASJC) codes

  • General Medicine

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